This has to be car-ried out on the physical layer link level and in the net-work system level context: 1 Link level simulations allow for the investigation of channel estimation, trackin
Trang 1R E S E A R C H Open Access
The Vienna LTE simulators - Enabling
reproducibility in wireless communications
research
Christian Mehlführer*, Josep Colom Ikuno, Michal Šimko, Stefan Schwarz, Martin Wrulich and Markus Rupp
Abstract
In this article, we introduce MATLAB-based link and system level simulation environments for UMTS Long-Term Evolution (LTE) The source codes of both simulators are available under an academic non-commercial use license, allowing researchers full access to standard-compliant simulation environments Owing to the open source
availability, the simulators enable reproducible research in wireless communications and comparison of novel algorithms In this study, we explain how link and system level simulations are connected and show how the link level simulator serves as a reference to design the system level simulator We compare the accuracy of the PHY modeling at system level by means of simulations performed both with bit-accurate link level simulations and PHY-model-based system level simulations We highlight some of the currently most interesting research questions for LTE, and explain by some research examples how our simulators can be applied
Keywords: LTE, MIMO, link level, system level, simulation, reproducible research
1 Introduction
Reproducibility is one of the pillars of scientific research
Although reproducibility has a long tradition in most
nature sciences and theoretical sciences, such as
mathe-matics, it is only recently that reproducible research has
become more and more important in the field of signal
processing [1,2] In contrast to results in fields of purely
theoretical sciences, results of signal processing research
articles can be reproduced only if a comprehensive
description of the investigated algorithms (including the
setting of all necessary parameters), as well as eventually
required input data are fully available Owing to the lack
of space, a fully comprehensive description of the
algo-rithm is often omitted in research articles Even if an
algorithm is explained in detail, for instance, by a
pseudo code, initialization values are often not fully
defined Moreover, it is often not possible to include in
an article all the necessary resources, such as data,
which were processed by the presented algorithms
Ide-ally, all resources, including source code of the
pre-sented algorithms, should be made available for
download to enable other researchers (and also
reviewers of articles) to reproduce the results presented Unfortunately, researcher’s reality does not resemble this ideal situation, a circumstance that has recently been quite openly complained about [3]
In the past few years, several researchers have started
to build up online resource databases in which simula-tion code and data are provided, see for example [4,5] However, it is still not a common practice in signal pro-cessing research We are furthermore convinced that reproducibility should also play an important role in the review process of an article Although thorough check-ing is very possibly impractical, it would make the pre-sented studies more transparent to the review process Reproducibility becomes even more important when the systems that are simulated become more and more complex, as it is the case in the evaluation of wireless communication systems When algorithms for wireless systems are evaluated, authors often claim to use a stan-dard-compliant transmission system and simply make reference to the corresponding technical specification Since technical specifications are usually extensive, including a cornucopia of options, it is not always clear which parts of a specification were actually implemented and which parts were omitted for the sake of simplicity
* Correspondence: chmehl@gmail.com
Institute of Telecommunications, Vienna University of Technology, Austria
© 2011 Mehlführer et al; licensee Springer This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
Trang 2reasons The situation of trying to reproduce someone
else’s results to compare them to one’s own algorithm
but not being able to do so (or only after extensive
effort to discover the unreported details of the actual
implementation) is familiar to most researchers
With-out access to the details of the implementation,
includ-ing all assumptions, comparisons of algorithms,
developed by different researchers, are very difficult, if
not impossible to carry out A way out of this dilemma
is offered by a publicly available simulation
environ-ment In this study, we present such an open-source
simulation environment that supports link and system
level simulations of the Universal Mobile
Telecommuni-cations System (UMTS) Long-Term Evolution (LTE),
specifically designed to support reproducibility The
development and publishing of this LTE simulation
environment is based on our previous very good
experi-ence with a WiMAX physical layer simulator [6]
Furthermore, such simulators can be used as a
refer-ence for validation of algorithms, for example, when
designing transmitter or receiver chips [7] We also have
used our simulators for generating LTE signals that are
required to include realistic signals in related research
[8], or as a reference for LTE-compliant measurements
In such cases, the simulator can serve not only as a data
pump, but also as a vehicle to evaluate the received data
LTE, the current evolutionary step in the third
Gen-eration Partnership Project (3GPP) roadmap for future
wireless cellular systems, was introduced in 3GPP
Release 8 [9] Besides the definition of the novel physical
layer, LTE also contains many other remarkable
innova-tions Most notable are (i) the redevelopment of the
sys-tem architecture, now called Syssys-tem Architecture
Evolution (SAE), (ii) the definition of network
self-orga-nization, and (iii) the introduction of home
base-sta-tions The main reasons for these profound changes in
the Radio Access Network (RAN) system design are to
provide higher spectral efficiency, lower delay (latency),
and more multi-user flexibility than the currently
deployed networks
In the development and standardization of LTE, as
well as in the implementation process of equipment
manufacturers, simulations are necessary to test and
optimize algorithms and procedures This has to be
car-ried out on the physical layer (link level) and in the
net-work (system level) context:
1) Link level simulations allow for the investigation of
channel estimation, tracking, and prediction algorithms,
as well as synchronization algorithms [10,11];
Multiple-Input Multiple-Output (MIMO) gains; Adaptive
Modu-lation and Coding (AMC); and feedback techniques
[12,13] Furthermore, receiver structures (typically
neglecting inter-cell interference and impact of
schedul-ing, as this increases simulation complexity and runtime
dramatically) [14], modeling of channel encoding and decoding [15], physical-layer modeling crucial, for sys-tem level simulations [16], and the like are typically ana-lyzed on link level Although MIMO broadcast channels have been investigated quite extensively over the past few years [17,18], there are still a lot of open questions that need to be resolved, both in theory and in practical implementation For example, LTE offers the flexibility
to adjust many transmission parameters, but it is not clear up to now how to exploit the available Degrees of Freedom (DoF) to achieve the optimum performance Some recent theoretical results point out how to pro-ceed in this matter [18,19], but practical results for LTE are still missing
2) System level simulations focus more on network-related issues, such as resource allocation and schedul-ing [20,21], multi-user handlschedul-ing, mobility management, admission control [22], interference management [23,24], and network planning optimization [25,26] Furthermore, in a multi-user oriented system, such as LTE, it is not directly clear which figures of merit should be used to assess the performance of the system The classical measures of (un)coded Bit Error Ratio (BER), (un)coded BLock Error Ratio (BLER), and throughput are not covering multi-user scenario proper-ties More comprehensive measures of the LTE perfor-mance are, for example, fairness, multi-user diversity, or DoF[27] However, these theoretical concepts have to
be mapped to performance values that can be evaluated
by means of simulations [28,29]
Around the world, many research facilities and ven-dors are investigating the above mentioned aspects of LTE For that purpose, commercially available simula-tors applied in industry [30-32], as well simulasimula-tors applied in academia [33], have been developed Also, probably all major equipment vendors have implemen-ted their own, proprietary simulators Regardless of the simulation tools being commercial/noncommercial, the development framework (C, C++, MATLAB, WM-SIM [33], ), or their claimed performance/flexibility, one fact
is shared by all of the simulators Their closed imple-mentation disables access to impleimple-mentation details and thus to any assumption that may have been included
As such, the reliability of the results relies purely on the faith of a proper implementation Independent valida-tion of results in such closed simulavalida-tion environments is not easy, very time-consuming, and often not feasible Since the results were obtained with closed tools, simply repeating the same experiment is a daunting task Transparency not only in the results, but also in the tools employed, thus greatly magnifies the credibility of the results
The two simulators [34,35] described in Sections 2 and 3 of this article are freely available at our homepage
Trang 3http://www.nt.tuwien.ac.at/ltesimulator/ under an open,
free for non-commercial academic use license, which
facilitates academic research and enables a closer
coop-eration between different universities and research
facil-ities In addition, developed algorithms can be shared
under the same license again, making the comparison of
algorithms easier, reproducible, and therefore refutable
and more credible To the best of the authors’
knowl-edge, our two simulators are the first to be published in
the context of LTE, including source code under an
aca-demic use license Thus, the simulators provide
oppor-tunities for many institutions to directly apply their
ideas and algorithms in the context of LTE The
avail-ability of the simulators, together with the possibility to
include links to the utilized simulator version and any
resources needed furthermore, enables researchers to
quickly reproduce published results [2]
The remainder of this article is organized as follows
In Sections 2 and 3, we describe the Vienna LTE
Simu-latorsand how they relate to each other In Section 4,
we provide a validation of the two simulators
Exemp-lary simulation results are shown in Section 5 Finally,
we conclude the article in Section 6
2 The Vienna LTE link level simulator
In this section, we describe the overall structure of the
Vienna LTE Link Level Simulator, currently (January
2011) released in version 1.6r917 Furthermore, we
pre-sent the capabilities of the simulator and provide some
examples of its application
A Structure of the simulator
The link level simulator can be divided into three basic
building blocks, namely, transmitter, channel model, and
receiver(see Figure 1) Depending on the type of
simula-tion, one or several instances of these basic building
blocks are employed The transmitter and receiver
blocks are linked by the channel model, which is used
to transmit the downlink data, while signaling and
uplink feedback is assumed to be error-free Since
signaling is stronger protected than data, by means of lower coding rates and/or lower-order modulations, the assumption of error-free signaling is in fact quite realis-tic Equivalently, errors on the signaling channels will only occur when the data channels are already facing
usually not targeted in investigations
In the downlink, the signaling information passed on
by the transmitter to the receiver contains coding, HARQ, scheduling, and precoding parameters In the uplink, Channel Quality Indicator (CQI), Precoding Matrix Indicator (PMI), and Rank Indicator (RI) are sig-nalled, which together form the Channel State Informa-tion (CSI) feedback All simulaInforma-tion scenarios (see Section 2-B) support the feedback of CQI, PMI, and RI, although it is also possible to set some or all of them to fixed values Such a setting is required for specific simu-lations, such as throughput evaluation of a single Modu-lation and Coding Scheme (MCS)
A standard-compliant implementation of the downlink control channels would not affect the overall structure
of our simulator and just requires the insertion of the control channels in the relevant resource elements [36]
On the other hand, non-error-free feedback transmis-sions would require a physical layer implementation of the LTE uplink, which is currently not in the scope of the simulator (A first release of the uplink, however, is currently being implemented in the simulator and will
be released soon.) 1) Transmitter The layout of the transmitter is shown in Figure 2, which
is also a graphical representation of the transmitter
Channel model
PDP-based channel or Winner+ channel trace
CSI feedback, ACK/NACKs Delay
signaling
coded/uncoded BER block error rate throughput
Figure 1 LTE link level simulator overall structure, as
implemented in the Vienna LTE link level simulator The
simulator comprises by one or more transmitter blocks, channel
modeling for each link, and receiver blocks The feedback channel is
implemented as a delayed error-free signaling channel.
per-user channel coding
of the data bits
random data bits generation data bits
allowed
coding params
precoding params
modulation params
symbol mapping
layer mapping, precoding
RB allocation OFDM symbol assembly
IFFT
transmitted signal signaling
user feedback
reference/sync symbols
CP insertion
Figure 2 LTE downlink transmitter implementation in the Vienna LTE link level simulator, as specified in [36-38].
Trang 4description defined in the TS36’ standard series [36-38].
Based on User Equipment (UE) feedback values, a
sche-duling algorithm assigns Resource Blocks (RBs) to UEs
and sets an appropriate MCS (coding rates between
0.076 and 0.926 with 4, 16, or 64-QAM modulation [38]),
the MIMO transmission mode (Transmit Diversity
(TxD), Open Loop Spatial Multiplexing (OLSM), or
Closed Loop Spatial Multiplexing (CLSM)), and the
pre-coding/number of spatial layers for all served users Such
a channel adaptive scheduling allows for the exploitation
of frequency diversity, time diversity, spatial diversity,
and multi-user diversity
Given the number of available DoF, the specific
imple-mentation of the scheduler algorithm has a large impact
on the system performance and has been a hot topic in
research [39-41] In Section 5-B, we provide
perfor-mance evaluations of several schedulers
2) Channel model
The Vienna LTE Link Level Simulator supports
block-and fast-fading channels In the block-fading case, the
channel is constant during the duration of one subframe
(1 ms) In the fast-fading case, time-correlated channel
impulse responses are generated for each sample of the
transmit signal Currently (January 2011), the simulator
supports the following channel models:
1) Additive White Gaussian Noise (AWGN);
2) Flat Rayleigh fading;
3) Power Delay Profile-based channel models, such
as ITU Pedestrian B, or ITU Vehicular A [42];
4) Winner Phase II+ [43]
The most sophisticated of these channel models is the
Winner Phase II+ model It is an evolution of the 3GPP
spatial channel model, and introduces additional
fea-tures, such as support for arbitrary 3D antenna patterns
3) Receiver
Figure 3 shows our implementation of the UE receiver
After disassembling the RBs according to the UE
resource allocation, MIMO Orthogonal Frequency
Divi-sion Multiplexing (OFDM) detection is carried out The
simulator currently supports Zero-Forcing (ZF), Linear
Minimum Mean Squared Error (LMMSE), and soft
sphere decoding as detection algorithms The detected
soft bits are decoded to obtain the data bits and several
figures of merit, such as coded/uncoded BER, BLER,
and throughput
Currently, four different types of channel estimators are
supported within the simulator: (i) Least Squares (LS), (ii)
Minimum Mean Squared Error (MMSE), (iii)
Approxi-mate LMMSE [44], and (iv) genie-driven (near) perfect
channel knowledge based on all transmitted symbols
LTE requires UE feedback to adapt the transmission
to the current channel conditions The LTE standard
specifies three feedback indicators for that purpose: CQI, RI, and PMI [36] The CQI is employed to choose the appropriate MCS, such as to achieve a predefined target BLER, whereas the RI and the PMI are utilized for MIMO pre-processing Specifically, the RI informs the eNodeB about the preferred number of parallel spa-tial data streams, while the PMI signals the preferred precoder that is stemming from a finite code book as specified in [36] Very similar feedback values are also employed in other systems such as WiMAX and WiFi The simulator provides algorithms that utilize the esti-mated channel coefficients to evaluate these feedback indicators [13] Researchers and engineers working on feedback algorithms can implement other algorithms using the provided feedback functions as a starting point to define their own functions
Given this receiver structure, the simulator allows the investigation of various aspects, such as frequency syn-chronization [45], channel estimation [44], or interfer-ence awareness [46]
B Complexity Link level simulators are in practice a direct standard-compliant implementation of the Physical (PHY) layer procedures, including segmentation, channel coding, MIMO, transmit signal generation, pilot patterns, and synchronization sequences Therefore, implementation complexity and simulation time are in general high To obtain a simulator with readable and maintainable code,
a high-level language (MATLAB) has been chosen This choice enabled us to develop the simulator in a fraction
of the time required for an implementation in other lan-guages such as C Furthermore, MATLAB ensures cross-platform compatibility While MATLAB is cer-tainly slower than C, by means of code optimization
signaling
throughput BER
BLER
resource block disassembling
RB allocation
decoded data bits user
feedback
CQI/PMI/RI feedback calculation
MIMO RX and OFDM detection
received signal
time-frequency resource block grid
CP removal
FFT
precoding
coding params channel decoding
channel estimation
Figure 3 LTE downlink receiver structure, as implemented in the Vienna LTE link level simulator.
Trang 5(vectorization) and parallelization by the MATLAB
Par-allel/Distributed Computing Toolbox, simulation
run-time can be greatly reduced Severely
difficult-to-vectorize and often-called functions are implemented in
C and linked to the MATLAB code by means of MEX
functions Such functions include the channel coding/
decoding [47], Cyclic Redundancy Check (CRC)
compu-tation [48], and soft sphere decoding
Furthermore, it is possible to adjust the scale of the
simulation to the specific needs This is achieved by
introducing three different simulation types with largely
different computational complexity (Figure 4):
1) Single-downlink
This simulation type only covers the link between one
eNodeB and one UE Such a set-up allows for the
inves-tigation of channel tracking, channel estimation [44],
synchronization [11,49], MIMO gains, AMC and
feed-back optimization [13], receiver structures [14]
modeling of channel encoding and decoding [15,50],
and physical layer modeling [51], which can be used for
system level abstraction of the physical layer To start a
simple single-downlink simulation, run the file
LTE_-sim_batch_single_downlink.m
2) Single-cell multi-user
This simulation covers the links between one eNodeB
and multiple UEs This set-up additionally allows for the
investigation of receiver structures that take into
account the influence of scheduling, multi-user MIMO
resource allocation, and multi-user gains Furthermore,
this set-up allows researchers to investigate practically
achievable multi-user rate regions In the current
imple-mentation, the simulator fully evaluates the receivers of
all users However, if receiver structures are being
investigated, the computational complexity of the simu-lation can considerably be reduced by only evaluating the user of interest In order to enable a functional sche-duler, it is sufficient to compute just the feedback para-meters for all other users To start a simple single-cell multi-user simulation, run the file LTE_sim_batch_sin-gle_cell_multi_user.m
3) Multi-cell multi-user This simulation is by far the computationally most demanding scenario and covers the links between multi-ple eNodeBs and UEs This set-up allows for the
techniques [52], interference management (including cooperative transmissions [53] and interference align-ment [54,55]), and network-based algorithms such as joint resource allocation and scheduling Furthermore, despite the vast computational efforts needed, such simulations are crucial to verify system level simulations
To start a simple multi-cell multi-user simulation, run the file LTE_sim _batch_multi_cell_multi_user.m The simulation time, which depends mainly on the desired precision and statistical accuracy of the simula-tion results, the selected bandwidth, the transmission mode, and the chosen modulation order, is for most users a crucial factor It should be noted that by a smart choice of the simulation settings, the simulation time can
be decreased (e.g., when investigating channel estimation performance, the smallest bandwidth can be sufficient)
3 The Vienna LTE system level simulator
In this section, we describe the overall structure of the Vienna LTE System Level Simulator, currently devel-oped (January 2011) version 1.3r427 We furthermore show how the PHY layer procedures have been abstracted in a low complexity manner
A Structure of the simulator
In system level simulations, the performance of a whole network is analyzed In LTE, such a network consists of
a multitude of eNodeBs that cover a specific area in which many mobile terminals are located and/or moving around While simulations of individual physical layer links allow for the investigation of MIMO gains, AMC feedback, modeling of the channel code, and retransmis-sions [13,44,45,50,56], it is not possible to reflect the effects of cell planning, scheduling, or interference in a large scale with dozens of eNodeBs and hundreds of users Simply performing physical layer simulations of the radio links between all terminals and base-stations is unfeasible for system level investigations because of the vast amount of computational power required Thus, the physical layer has to be abstracted by simplified models capturing its essential dynamics with high accu-racy at low complexity
single-downlink
single-cell multi-user
multi-cell multi-user
X2
Figure 4 Three possible scenarios in the Vienna LTE link level
simulator allow us to adjust the scale of the simulation
complexity: single-downlink, single-cell user, and
multi-cell multi-user.
Trang 6Based on the standard approach in the literature
[51,57], our simulator consists of two parts: (i) a link
measurement model, and (ii) a link performance model
The link measurement model reflects the link quality,
given by the UE measurement reports, and is required
to carry out link adaptation and resource allocation The
chosen link quality measure is evaluated per subcarrier
Based on the Signal to Interference and Noise Ratio
(SINR), the UE computes the feedback (PMI, RI, and
CQI), which is employed for link adaptation at the
eNo-deB as described in Section 2-A The scheduling
algo-rithm assigns resources to users to optimize the
performance of the system (e.g., in terms of throughput)
based on this feedback [21] Based on the link
measure-ment model, the link performance model predicts the
BLER of the link, based on the receiver SINR and the
transmission parameters (e.g., modulation and coding)
Figure 5 illustrates the interaction between the two
models and the several physical layer parameters
Implementation-wise, the simulator follows the
struc-ture shown in Figure 6 Each network element is
repre-sented by a suitable class object, whose interactions are
described below
In order to generate the network topology,
transmis-sion sites are generated, to which three eNodeBs are
appended, i.e., sectors, each containing a scheduler (see
Figure 6) In the simulator, traffic modeling assumes full
buffers in the downlink A scheduler assigns PHY
resources, precoding matrices, and a suitable MCS to
each UE attached to an eNodeB The actual assignment
depends on the scheduling algorithm and the received
UE feedback
At the UE side, the received subcarrier
post-equaliza-tion symbol SINR is calculated in the link measurement
model The SINR is determined by the signal,
interfer-ence, and noise power levels, which are dependent on
the cell layout (defined by the eNodeB positions, large-scale (macroscopic, macro-large-scale) pathloss, shadow fad-ing [58]), and the time-variant small-scale (microscopic, micro-scale) fading [59]
The CQI feedback report is calculated based on the subcarrier SINRs and the target transport BLER The CQI reports are generated by an SINR-to-CQI mapping [35] and made available to the eNodeB implementation via a feedback channel with adjustable delay At the transmitter, the appropriate MCS is selected by the CQI
to achieve the targeted BLER during the transmission Especially in high mobility scenarios, the feedback delay caused by computation and signaling timings can lead to
a performance degradation if the channel state changes significantly during the delay In the link performance model, an AWGN-equivalent SINR (gAWGN) is obtained via Mutual Information Effective Signal to Interference and Noise Ratio Mapping (MIESM) [60-62]
link performance curves [34,35] The BLER value acts as
a probability for computing ACK/NACKs, which are combined with the Transport Block (TB) size to compute the link throughput The simulation output consists of traces, containing link throughput and error ratios for each user, as well as cell aggregates, from which statistical distributions of throughputs and errors can be extracted
B Complexity One desirable functionality of a system level simulator is the ability to precalculate as many of the simulation
mobility management
link-performance model
micro-scale fading
interference structure
macro-scale fading antenna gain shadow fading
throughput error rates error distribution
traffic model
resource scheduling
strategy
precoding
base-station deployment
antenna gain pattern
tilt/azimuth
network layout
power allocation strategy
link-measurement model
link adaptation strategy
Figure 5 Schematic block diagram of the LTE system level
simulator Link quality is evaluated by means of the
link-measurement model, while the link-performance model maps it to
BLER and outputs link throughput and error distribution.
scheduler resource allocation
channel adaptation
attached UEs DL channels
eNodeBs
UEs
SINR-to-CQI mapping
SINR-to-BLER mapping BLER, throughput
noise
subcarrier SINR calculation & averaging signaling
downlink channels macroscopic pathloss shadow fading small scale fading
UE-specific
UL-Channel
Figure 6 Schematic class diagram showing the implementation
of the link-to-system model in the LTE System Level Simulator and depicting the relationship between the several elements/ classes in the simulator Implementation-wise, both the link-measurement and the link-performance model reside in the UE class.
Trang 7parameters as possible This not only reduces the
com-putational load while carrying out a simulation, but also
offers repeatability by loading an already partly
precalcu-lated scenario
The precalculations involved in the LTE system level
simulator are the generation of (i) eNodeB-dependent
large-scale pathloss maps, (ii) site-dependent shadow
fading maps, and (iii) time-dependent small-scale fading
traces for each eNodeB-UE pair
1) Pathloss and fading maps
The large-scale pathloss and the shadow fading are
modeled as position-dependent maps The large-scale
pathloss is calculated according to well-known models
[58,63] and combined with the antenna gain pattern of
the corresponding eNodeB Space-correlated shadow
fading is obtained from a log-normal random
distribu-tion using a low-complexity variant of the Cholesky
decomposition [64] Inter-site map correlation for
sha-dow fading is similarly obtained Figure 7 shows
exemp-lary large-scale pathloss and shadow fading maps
2) Time-dependent fading trace
While the large-scale pathloss and the shadow fading
are modeled as position-dependent trace, the small-scale
fading is modeled as a time-dependent trace The
calcu-lation of this latter trace is based on the transmitter
pre-coding, the small-scale fading MIMO channel matrix,
and the receive filter Currently, the receiver modeling is
based on a linear ZF receiver The small-scale fading
trace consists of the signal power and the interference
power after the receive filter The break-down into these
two parts significantly reduces the computational effort
since it avoids many complex multiplications required
when directly working with MIMO channel matrices on system level [16,35,51]
4 Validation of the simulators The validation of the simulators was performed in two steps First, in Section 4-A we compared the link level throughput with the minimum performance require-ments stated by 3GPP in the technical specification TS 36.101 [65] Second, in Section 4-B, we cross-validated the link and the system level simulators by comparing their results against each other Other means of valida-tion are being discussed in Secvalida-tion 4-C
A 3GPP minimum performance requirements The technical specification TS 36.101 [65] defines mini-mum performance requirements for a UE that utilizes a dual-antenna receiver These requirements have to be met by real devices and therefore have to be surpassed
by our simulator, in which not every conceivable influ-ential factor is incorporated.bSuch factors may include frequency and timing synchronization as well as other non-ideal effects, such as quantization or non-ideality of the manufactured physical components (e.g., I/Q imbal-ances, phase noise, and power amplifier nonlinearities)
In particular, TS 36.101 specifies reference measure-ment channels for the Physical Downlink Shared Chan-nel (PDSCH) (comprising bandwidth, AMC scheme, overhead, ) and propagation conditions (power delay profiles, Doppler frequencies, and antenna correlation) The considered simulation scenarios are completely spe-cified by referring to sections and test numbers in TS 36.101 For example, in TS 36.101 Section 8.2.1.1.1, the
receive antenna NR= 2 scenario are defined By refer-ring to test number one in Section 8.2.1.1.1 of TS 36.101, the AMC mode is defined as Quadrature Phase Shift Keying (QPSK) with a target coding rate of 1/3, Extended Vehicular A (EVehA) channel model with a Doppler frequency of 5 Hz, and low antenna correlation For our simulations presented in this article, we selected four test scenarios with a bandwidth of 10 MHz but dif-ferent transmit modes (single antenna port transmission, OLSM, and TxD), different AMC schemes, and different channel models Hybrid Automatic Repeat reQuest (HARQ) is supported by at most three retransmissions The most important parameters of the test scenarios are listed in Table 1 The first scenario (8.2.1.1.1/1) refers to the test scenario described above The OLSM scenario (8.2.1.3.2/1) utilizes a rank two transmission, that is, transmission of two spatial streams
Simulation results for the considered scenarios are shown in Figure 8 The dashed horizontal lines corre-spond to 70% of the maximum throughput values for which TS 36.101 defines a channel signal-to-noise ratio
x pos [m]
−1000 −500 0 500 1000
−1000
−800
−600
−400
−200
0
200
400
600
800
1000
70 80 90 100 110 120 130 140
−1000 −500 0 500 1000
−40
−30
−20
−10 0 10 20 30 40
x pos [m]
pathloss [dB] shadow fading [dB]
eNodeB
eNo deB
site-dependent: same for all eNodeBs in site eNodeB-dependent: one
independent pathloss map
per eNodeB
Figure 7 eNodeB- and site-dependance of the large-scale
pathloss and shadow fading Left: Large-scale pathloss and
antenna gain map [dB] corresponding to the lower-leftmost
eNodeB Right: space-correlated shadow fading corresponding to
the site [dB].
Trang 8(SNR) requirement (shown as crosses in Figure 8) For
all the considered test scenarios, the link level simulator
outperforms the minimum requirements by
approxi-mately 2-3 dB The small vertical bars within the
mar-kers in 8 are the 99% confidence intervals of the
simulated mean throughput Since the confidence
inter-vals are much smaller than the distances between the
individual throughput curves, we know that a repeated
simulation with different seeds of the random number
generators will lead to similar results and conclusions
Figure 8 can be reproduced by calling the script
simulator
B Link and system level cross-comparison
Next, we cross-compare the performance of the link and
system level simulators We consider a single user
sin-gle-cell scenario with different antenna configurations
and transmit modes, as summarized in Table 2
Depending on the channel conditions, we adapt the
AMC scheme, the transmission rank, and the
precod-ing matrices For this purpose, we utilize the UE
feedback schemes originally presented in [13] In order to create an equivalent simulation scenario on link and system level, we do not employ shadow fad-ing While on link level the SNR is usually directly specified, on system level the SNR is a function of the user location in the cell Without shadow fading, the user SNR on system level becomes a function of the distance between base-station and the user This can be utilized to indirectly select appropriate SNR values in the system level simulator The results of the link and system level comparisons are shown in Figure 9 For all the considered simulation scenarios,
we obtain an excellent match between the results of the two simulators, confirming the validity of our Link Error Prediction (LEP) model [50] on system level Figure 9 can be reproduced by running the script Reproducibility _LLvsSL_batch.m provided in the system level simulator package Further compari-sons between link and system level simulator results are shown in Section 5-B
Table 1 Test scenarios of 3GPP TS 36.101
8.2.1.1.1/1 8.2.1.1.1/8 8.2.1.2.1/1 8.2.1.3.2/1
20 15 10 5 0 -5 -10
-15
25
20
15
10
5
0
channel SNR [dB]
mean throughput [Mbit/s] 8.2.1.1.1/1
8.2.1.2.1/1 8.2.1.1.1/8 8.2.1.3.2/1
8.2.1.1.1/1
8.2.1.2.1/1 8.2.1.1.1/8 8.2.1.3.2/1
Figure 8 Throughput simulations of the test scenarios in 3GPP
TS 36.101 and comparison to the minimum performance
requirements (marked with crosses) The small vertical bars
within the circular markers indicate the 99% confidence intervals.
Reproducible by running Reproducibility_RAN_sims.m.
Table 2 Test scenarios for the cross-comparison of the link and system level simulators (SU CASE)
TU, typical urban channel model [75].
40 30
20 10
0 -10
10
8
6
4
2
0
channel SNR [dB]
link level system level link level system level
1x1 SISO 2x2 TxD
1x1 SISO 2x2 TxD
Figure 9 Cross-comparison of throughput results obtained with the link level and the system level simulators The small vertical bars within the circular markers indicate the 99% confidence intervals Reproducible by running Reproducibility_LLvsSL_batch.m.
Trang 9In Table 2 we compare the simulation times of the
link level simulator to those of the system level
simula-tor The simulations were conducted on a single core of
a 2.66 GHz Quad Core CPU The table also states the
simulation speed-up, defined as the ratio of the
simula-tion times required with the link level and the system
level simulator, respectively The speed-up of the system
level simulator for a Single-Input Single-Output (SISO)
system equals four This speed-up is rather small
because equalization, demodulation, and decoding (tasks
that are abstracted on system level) have low complexity
in a SISO system With increasing system complexity
also the speed-up increases We expected the largest
speed-up in the CLSM scenario, because it utilizes the
largest antenna configuration However, we measured
the largest speed-up of almost 18 in the OLSM
simula-tion scenario The reason is, that the precoder changes
from one subcarrier to the next, while in the CLSM
sce-nario, we assumed wideband feedback meaning that the
same precoder is employed on all the subcarriers [13]
The link level simulator supports the parallel
comput-ing capabilities of MATLAB With these features, it is
possible to run several MATLAB instances in parallel
on the multiple cores of a modern CPU The simulation
time of the link level simulator then decreases linearly
with the number of CPU cores, while the system level
simulator is currently not capable of parallel computing
C Further validation means
For a basic validation of the correctness of the results
produced by the simulator, we checked the uncoded
BER and throughput performance over frequency flat
Rayleigh fading and AWGN channels, as the theoretical
performance of these channels is known [66]
Further-more, we cross-checked our results with those produced
by the other industry simulators, by comparing with
corresponding publications of the 3GPP RAN WG1, e
g., [28,29] Still, an open issue is to prove a correct
func-tionality of each part of the simulator Evaluation of the
simulators has also been made possible for the whole
research community, allowing everybody to modify the
code to meet individual requirements and to check the
code for correctness [67-69], as the simulator’s
change-log reflects The first versions of the simulators have
been released in May 2009 (link level simulator) and in
March 2010 (system level simulator), respectively To
facilitate the exchange of bugs and/or results often
referred to as “crowdsourcing,” a forumc is also
pro-vided While the authors acknowledge this is not a
per-fect form of validation, neither is any other
5 Exemplary results
In this section, we show two exemplary simulation
results obtained with the Vienna LTE simulators First,
we present a link level throughput simulation in which
we compare the throughput of the different MIMO schemes to theoretic bounds Based on this simulation setup, researchers can investigate algorithms such as channel estimation, detection, or synchronization Sec-ond, we compare the performance of different state-of-the-art schedulers in a single-cell multi-user environ-ment These schedulers serve as reference for research-ers investigating advanced scheduling techniques
A Link level throughput Before presenting the link level throughput results of the different LTE MIMO schemes, we introduce theoretic bounds for the throughput We identify three bounds, namely, the mutual information, the channel capacity, and the so-called achievable mutual information Depending on the type of channel state information available at the transmitter (only receive SNR, full, or quantized), an ideal transmission system is expected to attain one of these bounds
1) Mutual information The mutual information is the theoretic bound for the data throughput if only the receive SNR but no further channel state information is available at the transmitter side [70]:
I =
Ntot
k=1
Bsublog2det
INR+ 1
σ2HkHHk
(1)
where Bsubdenotes the bandwidth occupied by a sin-gle data subcarrier,Hk the NR×NT(= number of receive antennas × number of transmit antennas) dimensional
n the energy of noise and interference at the receiver, Ntotthe total number of usable subcarriers, andINRan identity matrix of size equal to the number of receive antennas
NR In Equation (1), we normalized the transmit power
to one and the channel matrix according to
E{||Hk||2
2} = 1 Therefore, Equation (1) does not show a dependence on the transmit power and the number NT
of transmit antennas
The bandwidth Bsubof a subcarrier is calculated as
Bsub= N
Tsub− Tcp
subframe (usually equal to 14 when the normal cyclic prefix length is selected), Tsubthe subframe duration (1 ms), and Tcp the time required for the transmission of all cyclic prefixes within one subframe Note that we are calculating the mutual information for all usable subcar-riers of the OFDM system, thereby taking into account the loss in spectral efficiency caused by the guard band
Trang 10carriers If different transmission systems that apply
dif-ferent modulation formats are to be compared, however,
a fair comparison then requires calculating the mutual
information over the entire system bandwidth instead of
calculating it only over the usable bandwidth
Current communication systems employ adaptive
modulation and coding schemes to optimize the data
throughput For a specific receive SNR, assuming an
optimum receiver, the modulation and coding scheme
that maximizes the data throughput can be selected
Thus, if the transmitter knows the receive SNR, a
throughput equal to the mutual information should be
achieved
2) Channel capacity
For calculating the channel capacity of a frequency
selective MIMO channel [66], consider the singular
value decomposition of the channel matrix Hkscaled by
the standard deviation snof the additive white Gaussian
noise impairment:
1
σn
Hk= Uk
k
VHk ; with
k
= diag
λ k,m
m = 1 min(NR, NT)
(3)
The optimum, capacity-achieving, frequency-dependent
precoding at the transmitter is given by the unitary matrix
Vk If this precoding matrix is applied at the transmitter
and also the optimum receive filterUHk is employed, then
the MIMO channel is separated into min(NR, NT) (with
NR denoting the number of receive antennas and NTthe
number of transmit antennas) independent SISO channels,
each with a gain of
λ k,m,, m = 1 min(NR, NT), k = 1
Ntot The channel capacity is obtained by optimally
distri-buting the available transmit power over these parallel
SISO subchannels The optimum power distribution Pk, m
is the solution of the optimization problem:
C = max
P k,m
1
Ntot
min(NR,NT )
m=1
Ntot
k=1
log2(1 + P k,m λ k,m)
subject to
min(NR,NT )
m=1
Ntot
k=1
P k,m = P t
(4)
where the second equation is a transmit power
con-straint that ensures an average transmit power equal to
owing to the definition of
λ k,m,in Equation (3), the power distribution Pk, mand thus Ptremain
dimension-less We calculate the power coefficients maximizing
Equation (4) by the water-filling algorithm described in
[66] In order to achieve a throughput equal to the
chan-nel capacity, the transmitter needs full chanchan-nel state
information and has to apply the optimum precoder Furthermore, the receiver needs to apply the optimum receive filter to separate the parallel SISO subchannels 3) Achievable mutual information
Both mutual information and channel capacity do not consider system design losses caused, for example, by the transmission of cyclic prefix or reference symbols,
or the quantization of the transmitter precoding In order to obtain a tighter bound for the link level throughput, we therefore consider these effects in the definition of the so-called achievable mutual informa-tion In the case of open-loop transmission, in which space-time coding is employed at the transmitter, we obtain for the achievable mutual information:
I(OL)a =
Ntot
k=1
FBsub 1
NL log2det
INRNL+ 1
σ2 ˜Hk˜HH
k
, (5)
with NLdenoting the number of spatial transmission
effective channel matrix including the space-time coding [71] The factor F accounts for the inherent system losses due to the transmission of the cyclic prefix and the reference symbols In detail, the factor F is calcu-lated as
F = Tsub− Tcp
Tsub
CP loss
·Nsc· Ns/2− Nref
Nsc· Ns/2
reference symbols loss
,
(6)
resource block, and Nsc= 12 is the number of subcar-riers in each RB In LTE, the number of reference sym-bols depends on the number of transmit antennas Therefore, the efficiency factor F decreases with increas-ing number of transmit antennas (see Table 3)
In the case of closed-loop transmission, a
(defined in the standard) and applied to the transmit signal We calculate the achievable mutual information for closed-loop transmission as
I(CL)a = max
W∈W
Ntot
k=1
FBsublog2det
INR+σ12HkWWHHHk (7)
In Figure 10, the throughput of a 2×2 LTE system with 5 MHz bandwidth, perfect channel knowledge, and
Table 3 pilot symbols and efficiency factorF in LTE Transmit antennas
N T
Reference symbols
N ref
Efficiency factor F (%)
...INR+σ12HkWWHHHk (7)
In Figure 10, the throughput of a 2×2 LTE system with MHz bandwidth, perfect channel knowledge, and
Table pilot symbols and efficiency factorF in LTE Transmit